rions = [tobohr * float(ln.split()[i]) for ln in geom for i in range(3, 6)]
fions = [tobohr * float(ln.split()[i]) for ln in geom for i in range(6, 9)]
rionslist.append(rions)
fionslist.append(fions)
#### define Atomic Feature Mapping ####
afm = atomfm.AtomsFM(parameters)
#### define NN machine and weights for all atoms ####
nparameters = len(parameters)
sigmoid = lambda x: 1.0 / (1.0 + math.exp(-x))
sigmoidp = lambda x: math.exp(-x) / (math.exp(-x) + 1.0)**2
sigmoidpp = lambda x: 2 * math.exp(-2 * x) / (math.exp(
-x) + 1.0)**3 - math.exp(-x) / (math.exp(-x) + 1.0)**2
nn_machine = myfeedforward.MyFeedForward([nparameters, nparameters, 1],
[sigmoid, sigmoid, lambda x: x],
[sigmoidp, sigmoidp, lambda x: 1],
[sigmoidpp, sigmoidpp, lambda x: 0])
nn_weights = []
for ii in range(nion):
nn_weights += nn_machine.initial_w()
print("lenght w=", len(nn_weights))
nw = len(nn_weights) / nion
print("nw=", nw)
#nframes=200
for it0 in range(10):
alpha = 0.1 / nion
allframesfm = []
for frame in range(nframes):
framefm = []
def __init__(self, nsweeps, nlayers, parameterfilename, feidatafile,
energygradient0):
self.energygradient0 = energygradient0
parameters = []
with open(parameterfilename, 'r') as ff:
data = ff.read()
for line in data.split('\n'):
ss = line.split()
if (len(ss) > 1):
p = []
for s in ss:
if s.isdigit():
p += [int(s)]
elif isFloat(s):
p += [float(s)]
ok = p[0] in range(0, 6)
if (p[0] == 0): ok = ok and (len(p) == 2)
if (p[0] == 1): ok = ok and (len(p) == 2)
if (p[0] == 2): ok = ok and (len(p) == 4)
if (p[0] == 3): ok = ok and (len(p) == 3)
if (p[0] == 4): ok = ok and (len(p) == 5)
if (p[0] == 5): ok = ok and (len(p) == 5)
if ok: parameters.append(p)
#### define Atomic Feature Mapping ####
nparameters = len(parameters)
#self.nparameters = nparameters
self.afm = atomfm.AtomsFM(parameters)
#### define NN machine ####
self.nlayers = nlayers
#sigmoid = lambda x: 1.0/(1.0+math.exp(-x))
#sigmoidp = lambda x: math.exp(-x)/(math.exp(-x)+1.0)**2
#sigmoidpp = lambda x: 2*math.exp(-2*x)/(math.exp(-x)+1.0)**3 - math.exp(-x)/(math.exp(-x)+1.0)**2
sigmoid = lambda x: math.tanh(x)
sigmoidp = lambda x: (1.0 / math.cosh(x))**2
sigmoidpp = lambda x: -2.0 * math.tanh(x) * (1.0 / math.sech(x))**2
#anparameters = [nparameters]
#asigmoid = [lambda x: x]
#asigmoidp = [lambda x: 1]
#asigmoidpp = [lambda x: 0]
anparameters = []
asigmoid = []
asigmoidp = []
asigmoidpp = []
for i in range(nlayers):
anparameters.append(nparameters)
asigmoid.append(sigmoid)
asigmoidp.append(sigmoidp)
asigmoidpp.append(sigmoidpp)
anparameters.append(1)
asigmoid.append(lambda x: x)
asigmoidp.append(lambda x: 1)
asigmoidpp.append(lambda x: 0)
self.nn_machine = myfeedforward.MyFeedForward(anparameters, asigmoid,
asigmoidp, asigmoidpp)
#### read in number of atoms ####
with open(feidatafile, 'r') as ff:
feidata = ff.readline()
nion = int(feidata)
self.nion = nion
print("nion=", nion)
#### define NN machine weights for all atoms ####
self.nn_weights = []
for ii in range(nion):
self.nn_weights += self.nn_machine.initial_w()
self.nw = len(self.nn_weights) / nion
alpha = 0.05
for (symbols, rions, fions, energy) in read_fei_file(feidatafile):
#aalpha = alpha*random.random()
aalpha = alpha * random.random()
etmp = []
dedw = []
for ii in range(nion):
fm = self.afm(rions, ii)
print "fm=", fm
eee = self.nn_machine.dyoutdw_gradient(
fm, self.nn_weights[ii * self.nw:(ii + 1) * self.nw])
etmp += eee[0]
dedw += eee[1]
#print "etmp=",ii,eee[0],energy
error = math.sqrt((sum(etmp) - energy)**2)
derror1detmp = 2.0 * (sum(etmp) - energy)
for i in range(len(self.nn_weights)):
self.nn_weights[i] -= aalpha * derror1detmp * dedw[i]
self.nn_machine.print_w(self.nn_weights[0])
print("Checking Energies and Forces")
#nion3 = 3*nion
frame = 1
sumerror = 0.0
maxerror = 0.0
for (symbols, rions, fions, energy) in read_fei_file(feidatafile):
etmp0 = []
#force3 = [0.0]*nion3
for ii in range(nion):
fm = self.afm(rions, ii)
ee0 = self.nn_machine.evaluate(
fm, self.nn_weights[ii * self.nw:(ii + 1) * self.nw])
etmp0 += ee0
#fafm = self.afm.Egradients(rions,ii)
#eee = self.nn_machine.evaluate(fafm[0],self.nn_weights[ii*self.nw:(ii+1)*self.nw])
#fff = self.nn_machine.gradients_evaluate(fafm[0],self.nn_weights[ii*self.nw:(ii+1)*self.nw])
#esum += eee[0]
#for jj in range(nion3):
# for k in range(nparameters):
# force3[jj] -= fafm[1][jj + k*nion3]*fff[k]
error = math.sqrt((sum(etmp0) - energy)**2)
print frame, sum(etmp0), energy, error
sumerror += error
if (error > maxerror): maxerror = error
frame += 1
print(" - average error=", sumerror / frame, " maxerror=", maxerror)